Search

Self-monitoring is one of the most widely used and widely researched strategies for improving student behavior. However, specific research-based guidance about how to design effective self-monitoring interventions and to whom they should be delivered does not yet exist. To this end, we examined how various treatment components and participant characteristics moderated response to self-monitoring interventions. We included 66 single-case studies on academic engagement and 21 single-case studies on disruptive behavior. These studies included 290 participants with challenging behavior, 183 of whom had a disability. After extracting raw data from original studies, we analyzed data using multilevel modeling for each dependent variable (i.e., academic engagement, disruptive behavior). Across both dependent variables, student age and educational setting impacted treatment effects, as did the inclusion of goal-setting, feedback, and reinforcement. Based on our findings, we describe implications related to designing self-monitoring interventions. We also discuss limitations and future directions.

School-Wide Positive Behavior Interventions and Supports (SWPBIS) relies on effective implementation of Tier 1 practices to ensure accurate identification of students in need of more intensive supports at Tier 2 or Tier 3. While measures of school-level fidelity are widely used, measures of classroom-level implementation of Tier 1 supports are less common. If classroom levels of Tier 1 supports are variable, schools may identify students for Tier 2 supports when, instead, teachers need support implementing Tier 1 in classrooms. The purpose of this case study was to assess the impact of a self-monitoring intervention, Monitoring Behavior on the Go (MoBeGo), on the academic engagement and disruptive behavior of a middle school student. Initially, the impact of the self-monitoring intervention was inadequate. In the context of evaluating the impact of a Tier 2 intervention, we identified an opportunity to assess whether a class-wide Tier 1 program, Class-Wide Function-Related Intervention Teams (CW-FIT), enhanced the effectiveness of the Tier 2 intervention. Using an A-B-BC-B-BC design, we compared the effectiveness of a Tier 2 intervention alone with a combined Tier 1 + Tier 2 intervention. When the class-wide Tier 1 program was layered on top of the Tier 2 intervention, the student's academic engagement showed an increase in level and stability.

Check-in check-out (CICO) is a widely implemented program aimed at increasing prosocial behavior. Aligned with the logic of Tier 2 supports, CICO has a standard protocol meant to be implemented in a standardized fashion across all students. However, as CICO is not effective for all at-risk students, it is quite common to find researchers implementing adapted versions of CICO. The purpose of this review was to explore the types of, rationale for, and timing of adaptations made to CICO. Results showed that 71% of studies using CICO made adaptations to core components. While most of these adaptations were made at the onset of treatment, we found little evidence to suggest researchers had a systematic process for selecting adaptations. As schools continue to seek the most efficient and effective means to provide behavior supports to students, adaptive intervention planning is an important process to consider.

Self-monitoring is one of the most widely used and widely researched strategies for improving student behavior. However, specific research-based guidance about how to design effective self-monitoring interventions and to whom they should be delivered does not yet exist. To this end, we examined how various treatment components and participant characteristics moderated response to self-monitoring interventions. We included 66 single-case studies on academic engagement and 21 single-case studies on disruptive behavior. These studies included 290 participants with challenging behavior, 183 of whom had a disability. After extracting raw data from original studies, we analyzed data using multilevel modeling for each dependent variable (i.e., academic engagement, disruptive behavior). Across both dependent variables, student age and educational setting impacted treatment effects, as did the inclusion of goal-setting, feedback, and reinforcement. Based on our findings, we describe implications related to designing self-monitoring interventions. We also discuss limitations and future directions. [ABSTRACT FROM AUTHOR]

School-Wide Positive Behavior Interventions and Supports (SWPBIS) relies on effective implementation of Tier 1 practices to ensure accurate identification of students in need of more intensive supports at Tier 2 or Tier 3. While measures of school-level fidelity are widely used, measures of classroom-level implementation of Tier 1 supports are less common. If classroom levels of Tier 1 supports are variable, schools may identify students for Tier 2 supports when, instead, teachers need support implementing Tier 1 in classrooms. The purpose of this case study was to assess the impact of a self-monitoring intervention, Monitoring Behavior on the Go (MoBeGo), on the academic engagement and disruptive behavior of a middle school student. Initially, the impact of the self-monitoring intervention was inadequate. In the context of evaluating the impact of a Tier 2 intervention, we identified an opportunity to assess whether a class-wide Tier 1 program, Class-Wide Function-Related Intervention Teams (CW-FIT), enhanced the effectiveness of the Tier 2 intervention. Using an A-B-BC-B-BC design, we compared the effectiveness of a Tier 2 intervention alone with a combined Tier 1 + Tier 2 intervention. When the class-wide Tier 1 program was layered on top of the Tier 2 intervention, the student's academic engagement showed an increase in level and stability. [ABSTRACT FROM AUTHOR]

Check-in check-out (CICO) is a widely implemented program aimed at increasing prosocial behavior. Aligned with the logic of Tier 2 supports, CICO has a standard protocol meant to be implemented in a standardized fashion across all students. However, as CICO is not effective for all at-risk students, it is quite common to find researchers implementing adapted versions of CICO. The purpose of this review was to explore the types of, rationale for, and timing of adaptations made to CICO. Results showed that 71% of studies using CICO made adaptations to core components. While most of these adaptations were made at the onset of treatment, we found little evidence to suggest researchers had a systematic process for selecting adaptations. As schools continue to seek the most efficient and effective means to provide behavior supports to students, adaptive intervention planning is an important process to consider. [ABSTRACT FROM AUTHOR]